LLMs have had a reasoning setting for a while now. The reasoning is what allows them to go through and test each output to see which output/answer ends up aligning with known context the best (whether that be constraints that steer it away from some outputs, or information/hints that steer it towards the desired output).

The problem with the tutor's question is that they're going to AI to answer this question and not asking underlying questions like "what makes a square a square" and "what makes a rectangle a rectangle", then doing their own thinking to determine if a square's qualifications also fit inside the qualifications to be a rectangle.

Heck, even if you just feed the underlying into AI it'll reason the answer in a logical manner:

> In standard geometry, A square is a quadrilateral with 4 equal sides and 4 right angles (90° each). A rectangle is a quadrilateral with 4 right angles. Its opposite sides are equal and parallel, but all four sides do not have to be equal. So the key distinction is: Rectangle: four right angles. Square: four right angles plus four equal sides. > > That means every square is a rectangle, but not every rectangle is a square.

Asking LLMs to just give you answers is the real antipattern. But if you remember that the only thing it's good at is finding patterns given context/constraints, it can be an extremely good tool.

Also, I have a feeling the vast majority of the populus is using free versions of Claude or ChatGPT. Thankfully recently ChatGPT Free is GPT-5.6 Luna, but before the response qualities and capabilities of the free chat output were less than stellar.